Overview

Dataset statistics

Number of variables22
Number of observations4060
Missing cells34944
Missing cells (%)39.1%
Duplicate rows17
Duplicate rows (%)0.4%
Total size in memory729.5 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported1
Categorical4
Numeric15

Alerts

Region has constant value ""Constant
Division has constant value ""Constant
Games_Level has constant value ""Constant
Qualifier has constant value ""Constant
Dataset has 17 (0.4%) duplicate rowsDuplicates
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Chad1000x (s) is highly overall correlated with Filthy 50 (s) and 1 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 3 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Clean and Jerk (lbs) and 6 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Chad1000x (s) and 6 other fieldsHigh correlation
Fran (s) is highly overall correlated with Back Squat (lbs) and 8 other fieldsHigh correlation
Grace (s) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Helen (s) is highly overall correlated with Fight Gone Bad and 6 other fieldsHigh correlation
L1 Benchmark (s) is highly overall correlated with Chad1000x (s) and 4 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Filthy 50 (s) and 3 other fieldsHigh correlation
Rank is highly overall correlated with Clean and Jerk (lbs) and 7 other fieldsHigh correlation
Run 5k (s) is highly overall correlated with Helen (s)High correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Affiliate has 458 (11.3%) missing valuesMissing
Country has 4060 (100.0%) missing valuesMissing
Back Squat (lbs) has 271 (6.7%) missing valuesMissing
Clean and Jerk (lbs) has 297 (7.3%) missing valuesMissing
Deadlift (lbs) has 264 (6.5%) missing valuesMissing
Snatch (lbs) has 365 (9.0%) missing valuesMissing
Fight Gone Bad has 3180 (78.3%) missing valuesMissing
Max Pull-ups has 2270 (55.9%) missing valuesMissing
Chad1000x (s) has 4011 (98.8%) missing valuesMissing
L1 Benchmark (s) has 4046 (99.7%) missing valuesMissing
Filthy 50 (s) has 3523 (86.8%) missing valuesMissing
Fran (s) has 1594 (39.3%) missing valuesMissing
Grace (s) has 2163 (53.3%) missing valuesMissing
Helen (s) has 2851 (70.2%) missing valuesMissing
Run 5k (s) has 2491 (61.4%) missing valuesMissing
Sprint 400m (s) has 3100 (76.4%) missing valuesMissing
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-18 02:36:38.260135
Analysis finished2024-02-18 02:37:00.014035
Duration21.75 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct4027
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:00.163045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length13.593103
Min length4

Characters and Unicode

Total characters55188
Distinct characters98
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3994 ?
Unique (%)98.4%

Sample

1st rowTola Morakinyo
2nd rowAndrea Barbotti
3rd rowNiklas Hecht
4th rowSam Stewart
5th rowNick Tunzi
ValueCountFrequency (%)
michael 60
 
0.7%
ryan 55
 
0.7%
matthew 48
 
0.6%
thomas 47
 
0.6%
tyler 46
 
0.5%
daniel 44
 
0.5%
james 42
 
0.5%
david 42
 
0.5%
kyle 40
 
0.5%
john 40
 
0.5%
Other values (4508) 7980
94.5%
2024-02-17T21:37:00.503952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5035
 
9.1%
e 4946
 
9.0%
4385
 
7.9%
n 3948
 
7.2%
r 3742
 
6.8%
o 3470
 
6.3%
i 3316
 
6.0%
l 2533
 
4.6%
s 2233
 
4.0%
t 2003
 
3.6%
Other values (88) 19577
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42200
76.5%
Uppercase Letter 8526
 
15.4%
Space Separator 4385
 
7.9%
Dash Punctuation 48
 
0.1%
Other Punctuation 28
 
0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5035
11.9%
e 4946
11.7%
n 3948
9.4%
r 3742
 
8.9%
o 3470
 
8.2%
i 3316
 
7.9%
l 2533
 
6.0%
s 2233
 
5.3%
t 2003
 
4.7%
h 1468
 
3.5%
Other values (38) 9506
22.5%
Uppercase Letter
ValueCountFrequency (%)
M 830
 
9.7%
J 701
 
8.2%
S 640
 
7.5%
C 626
 
7.3%
A 590
 
6.9%
B 575
 
6.7%
D 506
 
5.9%
R 503
 
5.9%
L 384
 
4.5%
G 382
 
4.5%
Other values (35) 2789
32.7%
Other Punctuation
ValueCountFrequency (%)
' 16
57.1%
. 12
42.9%
Space Separator
ValueCountFrequency (%)
4385
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50709
91.9%
Common 4462
 
8.1%
Greek 17
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5035
 
9.9%
e 4946
 
9.8%
n 3948
 
7.8%
r 3742
 
7.4%
o 3470
 
6.8%
i 3316
 
6.5%
l 2533
 
5.0%
s 2233
 
4.4%
t 2003
 
3.9%
h 1468
 
2.9%
Other values (71) 18015
35.5%
Greek
ValueCountFrequency (%)
Γ 3
17.6%
Ι 3
17.6%
Σ 2
11.8%
Λ 1
 
5.9%
Η 1
 
5.9%
Δ 1
 
5.9%
Ϊ 1
 
5.9%
Α 1
 
5.9%
Ο 1
 
5.9%
Ρ 1
 
5.9%
Other values (2) 2
11.8%
Common
ValueCountFrequency (%)
4385
98.3%
- 48
 
1.1%
' 16
 
0.4%
. 12
 
0.3%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55039
99.7%
None 149
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5035
 
9.1%
e 4946
 
9.0%
4385
 
8.0%
n 3948
 
7.2%
r 3742
 
6.8%
o 3470
 
6.3%
i 3316
 
6.0%
l 2533
 
4.6%
s 2233
 
4.1%
t 2003
 
3.6%
Other values (47) 19428
35.3%
None
ValueCountFrequency (%)
é 30
20.1%
á 17
 
11.4%
í 12
 
8.1%
ö 9
 
6.0%
ó 8
 
5.4%
ü 6
 
4.0%
ñ 6
 
4.0%
ä 5
 
3.4%
ç 5
 
3.4%
å 4
 
2.7%
Other values (31) 47
31.5%

Affiliate
Text

MISSING 

Distinct2577
Distinct (%)71.5%
Missing458
Missing (%)11.3%
Memory size63.4 KiB
2024-02-17T21:37:00.751007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length17.079123
Min length10

Characters and Unicode

Total characters61519
Distinct characters92
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1889 ?
Unique (%)52.4%

Sample

1st rowCrossFit East Nashville
2nd rowCrossFit Senigallia
3rd rowCrossFit OBA
4th rowCrossFit Walleye
5th rowFort to Fort CrossFit
ValueCountFrequency (%)
crossfit 3602
42.5%
city 56
 
0.7%
the 28
 
0.3%
west 25
 
0.3%
east 24
 
0.3%
north 24
 
0.3%
valley 22
 
0.3%
south 21
 
0.2%
fort 20
 
0.2%
and 17
 
0.2%
Other values (2773) 4646
54.8%
2024-02-17T21:37:01.119050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 8279
13.5%
o 5381
 
8.7%
r 5368
 
8.7%
i 5246
 
8.5%
t 5071
 
8.2%
4881
 
7.9%
C 4100
 
6.7%
F 3870
 
6.3%
e 2600
 
4.2%
a 2244
 
3.6%
Other values (82) 14479
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43102
70.1%
Uppercase Letter 12516
 
20.3%
Space Separator 4883
 
7.9%
Decimal Number 915
 
1.5%
Other Punctuation 69
 
0.1%
Dash Punctuation 24
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Open Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 8279
19.2%
o 5381
12.5%
r 5368
12.5%
i 5246
12.2%
t 5071
11.8%
e 2600
 
6.0%
a 2244
 
5.2%
n 1661
 
3.9%
l 1363
 
3.2%
u 825
 
1.9%
Other values (33) 5064
11.7%
Uppercase Letter
ValueCountFrequency (%)
C 4100
32.8%
F 3870
30.9%
S 486
 
3.9%
B 392
 
3.1%
T 330
 
2.6%
A 323
 
2.6%
M 301
 
2.4%
L 276
 
2.2%
R 273
 
2.2%
P 256
 
2.0%
Other values (21) 1909
15.3%
Decimal Number
ValueCountFrequency (%)
1 160
17.5%
0 157
17.2%
2 104
11.4%
5 89
9.7%
3 83
9.1%
7 77
8.4%
4 76
8.3%
6 62
 
6.8%
8 55
 
6.0%
9 52
 
5.7%
Other Punctuation
ValueCountFrequency (%)
' 38
55.1%
. 28
40.6%
& 3
 
4.3%
Space Separator
ValueCountFrequency (%)
4881
> 99.9%
  2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55618
90.4%
Common 5901
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 8279
14.9%
o 5381
9.7%
r 5368
9.7%
i 5246
9.4%
t 5071
9.1%
C 4100
 
7.4%
F 3870
 
7.0%
e 2600
 
4.7%
a 2244
 
4.0%
n 1661
 
3.0%
Other values (64) 11798
21.2%
Common
ValueCountFrequency (%)
4881
82.7%
1 160
 
2.7%
0 157
 
2.7%
2 104
 
1.8%
5 89
 
1.5%
3 83
 
1.4%
7 77
 
1.3%
4 76
 
1.3%
6 62
 
1.1%
8 55
 
0.9%
Other values (8) 157
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61445
99.9%
None 74
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 8279
13.5%
o 5381
 
8.8%
r 5368
 
8.7%
i 5246
 
8.5%
t 5071
 
8.3%
4881
 
7.9%
C 4100
 
6.7%
F 3870
 
6.3%
e 2600
 
4.2%
a 2244
 
3.7%
Other values (59) 14405
23.4%
None
ValueCountFrequency (%)
ä 21
28.4%
é 9
12.2%
í 7
 
9.5%
ú 6
 
8.1%
á 4
 
5.4%
ã 4
 
5.4%
ü 4
 
5.4%
Ö 2
 
2.7%
ō 2
 
2.7%
  2
 
2.7%
Other values (13) 13
17.6%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4060
Missing (%)100.0%
Memory size63.4 KiB

Region
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
worldwide
4060 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters36540
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 4060
100.0%

Length

2024-02-17T21:37:01.260210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:37:01.355508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 4060
100.0%

Most occurring characters

ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36540
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 36540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Men
4060 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12180
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMen
2nd rowMen
3rd rowMen
4th rowMen
5th rowMen

Common Values

ValueCountFrequency (%)
Men 4060
100.0%

Length

2024-02-17T21:37:01.444495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:37:01.529889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
men 4060
100.0%

Most occurring characters

ValueCountFrequency (%)
M 4060
33.3%
e 4060
33.3%
n 4060
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8120
66.7%
Uppercase Letter 4060
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4060
50.0%
n 4060
50.0%
Uppercase Letter
ValueCountFrequency (%)
M 4060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 4060
33.3%
e 4060
33.3%
n 4060
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 4060
33.3%
e 4060
33.3%
n 4060
33.3%

Rank
Real number (ℝ)

HIGH CORRELATION 

Distinct4013
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35295.339
Minimum2
Maximum157102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:01.631885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2493.8
Q113721
median27899
Q348811.5
95-th percentile93330.65
Maximum157102
Range157100
Interquartile range (IQR)35090.5

Descriptive statistics

Standard deviation29132.85
Coefficient of variation (CV)0.82540218
Kurtosis1.8045089
Mean35295.339
Median Absolute Deviation (MAD)16924.5
Skewness1.3180426
Sum1.4329908 × 108
Variance8.4872294 × 108
MonotonicityIncreasing
2024-02-17T21:37:01.766271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6120 2
 
< 0.1%
5539 2
 
< 0.1%
63522 2
 
< 0.1%
105088 2
 
< 0.1%
16654 2
 
< 0.1%
29664 2
 
< 0.1%
12096 2
 
< 0.1%
20189 2
 
< 0.1%
22222 2
 
< 0.1%
74148 2
 
< 0.1%
Other values (4003) 4040
99.5%
ValueCountFrequency (%)
2 1
< 0.1%
56 1
< 0.1%
66 1
< 0.1%
68 1
< 0.1%
88 1
< 0.1%
116 1
< 0.1%
130 1
< 0.1%
138 1
< 0.1%
139 1
< 0.1%
151 1
< 0.1%
ValueCountFrequency (%)
157102 1
< 0.1%
155374 1
< 0.1%
153679 1
< 0.1%
153121 1
< 0.1%
152619 1
< 0.1%
150939 1
< 0.1%
149948 1
< 0.1%
149633 1
< 0.1%
149448 1
< 0.1%
149414 1
< 0.1%

Games_Level
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
worldwide
4060 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters36540
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 4060
100.0%

Length

2024-02-17T21:37:01.885634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:37:01.970712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 4060
100.0%

Most occurring characters

ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36540
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 36540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 8120
22.2%
d 8120
22.2%
o 4060
11.1%
r 4060
11.1%
l 4060
11.1%
i 4060
11.1%
e 4060
11.1%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
open
4060 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16240
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowopen
2nd rowopen
3rd rowopen
4th rowopen
5th rowopen

Common Values

ValueCountFrequency (%)
open 4060
100.0%

Length

2024-02-17T21:37:02.060462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:37:02.145455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
open 4060
100.0%

Most occurring characters

ValueCountFrequency (%)
o 4060
25.0%
p 4060
25.0%
e 4060
25.0%
n 4060
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16240
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4060
25.0%
p 4060
25.0%
e 4060
25.0%
n 4060
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16240
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4060
25.0%
p 4060
25.0%
e 4060
25.0%
n 4060
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4060
25.0%
p 4060
25.0%
e 4060
25.0%
n 4060
25.0%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct252
Distinct (%)6.7%
Missing271
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean361.25815
Minimum4
Maximum709.88764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:02.244929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile253.5313
Q1319.6699
median363.7623
Q3405
95-th percentile462.9702
Maximum709.88764
Range705.88764
Interquartile range (IQR)85.3301

Descriptive statistics

Standard deviation65.803172
Coefficient of variation (CV)0.18215
Kurtosis0.93112634
Mean361.25815
Median Absolute Deviation (MAD)41.2377
Skewness-0.11884524
Sum1368807.1
Variance4330.0574
MonotonicityNot monotonic
2024-02-17T21:37:02.632015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405 177
 
4.4%
365 148
 
3.6%
385 129
 
3.2%
330.693 126
 
3.1%
352.7392 111
 
2.7%
315 106
 
2.6%
335 104
 
2.6%
308.6468 102
 
2.5%
396.8316 98
 
2.4%
374.7854 96
 
2.4%
Other values (242) 2592
63.8%
(Missing) 271
 
6.7%
ValueCountFrequency (%)
4 1
< 0.1%
100 1
< 0.1%
110 1
< 0.1%
115 1
< 0.1%
119.04948 1
< 0.1%
120 1
< 0.1%
125 1
< 0.1%
126 1
< 0.1%
130 1
< 0.1%
135 1
< 0.1%
ValueCountFrequency (%)
709.88764 1
< 0.1%
640 1
< 0.1%
625 1
< 0.1%
605 1
< 0.1%
600 1
< 0.1%
599.65664 1
< 0.1%
585 1
< 0.1%
575 1
< 0.1%
570 1
< 0.1%
565 1
< 0.1%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct226
Distinct (%)6.0%
Missing297
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean266.88256
Minimum10
Maximum425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:02.765018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile187.3927
Q1237
median265
Q3299.82832
95-th percentile340
Maximum425
Range415
Interquartile range (IQR)62.82832

Descriptive statistics

Standard deviation46.489269
Coefficient of variation (CV)0.17419373
Kurtosis1.1115676
Mean266.88256
Median Absolute Deviation (MAD)30
Skewness-0.38842023
Sum1004279.1
Variance2161.2521
MonotonicityNot monotonic
2024-02-17T21:37:02.890288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275 175
 
4.3%
265 156
 
3.8%
245 148
 
3.6%
315 135
 
3.3%
285 134
 
3.3%
264.5544 132
 
3.3%
255 128
 
3.2%
286.6006 118
 
2.9%
235 113
 
2.8%
242.5082 108
 
2.7%
Other values (216) 2416
59.5%
(Missing) 297
 
7.3%
ValueCountFrequency (%)
10 2
< 0.1%
28 1
 
< 0.1%
30 1
 
< 0.1%
56 1
 
< 0.1%
64 1
 
< 0.1%
70 1
 
< 0.1%
94.79866 1
 
< 0.1%
95 1
 
< 0.1%
99.2079 1
 
< 0.1%
105 3
0.1%
ValueCountFrequency (%)
425 1
 
< 0.1%
415 1
 
< 0.1%
400 2
< 0.1%
399.03622 2
< 0.1%
397 1
 
< 0.1%
395 1
 
< 0.1%
390 1
 
< 0.1%
385 4
0.1%
380 3
0.1%
376.99002 1
 
< 0.1%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct248
Distinct (%)6.5%
Missing264
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean436.32762
Minimum6
Maximum981.0559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:03.022699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile325
Q1396.8316
median440.924
Q3485
95-th percentile545
Maximum981.0559
Range975.0559
Interquartile range (IQR)88.1684

Descriptive statistics

Standard deviation69.32279
Coefficient of variation (CV)0.15887784
Kurtosis2.5168159
Mean436.32762
Median Absolute Deviation (MAD)44.076
Skewness-0.21745508
Sum1656299.7
Variance4805.6493
MonotonicityNot monotonic
2024-02-17T21:37:03.153573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
440.924 177
 
4.4%
405 170
 
4.2%
455 163
 
4.0%
425 132
 
3.3%
500 128
 
3.2%
462.9702 114
 
2.8%
418.8778 112
 
2.8%
396.8316 108
 
2.7%
435 104
 
2.6%
415 99
 
2.4%
Other values (238) 2489
61.3%
(Missing) 264
 
6.5%
ValueCountFrequency (%)
6 1
< 0.1%
10 1
< 0.1%
95 1
< 0.1%
115 1
< 0.1%
120 1
< 0.1%
138.89106 1
< 0.1%
140 1
< 0.1%
150 1
< 0.1%
160 1
< 0.1%
165 1
< 0.1%
ValueCountFrequency (%)
981.0559 1
 
< 0.1%
680 1
 
< 0.1%
675 1
 
< 0.1%
661.386 1
 
< 0.1%
655 1
 
< 0.1%
650 4
0.1%
643.74904 1
 
< 0.1%
635 2
< 0.1%
625 2
< 0.1%
620 3
0.1%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct204
Distinct (%)5.5%
Missing365
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean208.7681
Minimum1
Maximum489.42564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:03.282075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile143.3003
Q1185
median209.4389
Q3235
95-th percentile275
Maximum489.42564
Range488.42564
Interquartile range (IQR)50

Descriptive statistics

Standard deviation40.869739
Coefficient of variation (CV)0.19576621
Kurtosis1.3274039
Mean208.7681
Median Absolute Deviation (MAD)25.5611
Skewness-0.1993991
Sum771398.12
Variance1670.3356
MonotonicityNot monotonic
2024-02-17T21:37:03.435753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 190
 
4.7%
205 174
 
4.3%
185 152
 
3.7%
215 144
 
3.5%
220.462 135
 
3.3%
245 112
 
2.8%
235 111
 
2.7%
198.4158 107
 
2.6%
200 99
 
2.4%
176.3696 95
 
2.3%
Other values (194) 2376
58.5%
(Missing) 365
 
9.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
10 1
 
< 0.1%
20 1
 
< 0.1%
38 1
 
< 0.1%
45 1
 
< 0.1%
50 2
 
< 0.1%
55.1155 1
 
< 0.1%
65 1
 
< 0.1%
70 5
0.1%
75 1
 
< 0.1%
ValueCountFrequency (%)
489.42564 1
 
< 0.1%
415 1
 
< 0.1%
365 1
 
< 0.1%
340 1
 
< 0.1%
326 2
< 0.1%
315.26066 1
 
< 0.1%
315 4
0.1%
310.85142 1
 
< 0.1%
308.6468 4
0.1%
308 4
0.1%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct260
Distinct (%)29.5%
Missing3180
Missing (%)78.3%
Infinite0
Infinite (%)0.0%
Mean337.34545
Minimum42
Maximum581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:03.565051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile233.95
Q1300
median336
Q3376
95-th percentile443.15
Maximum581
Range539
Interquartile range (IQR)76

Descriptive statistics

Standard deviation63.627587
Coefficient of variation (CV)0.18861255
Kurtosis0.86101543
Mean337.34545
Median Absolute Deviation (MAD)38
Skewness0.01603016
Sum296864
Variance4048.4698
MonotonicityNot monotonic
2024-02-17T21:37:03.696531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352 12
 
0.3%
360 12
 
0.3%
358 11
 
0.3%
306 10
 
0.2%
355 10
 
0.2%
400 10
 
0.2%
313 10
 
0.2%
334 9
 
0.2%
305 9
 
0.2%
337 9
 
0.2%
Other values (250) 778
 
19.2%
(Missing) 3180
78.3%
ValueCountFrequency (%)
42 1
< 0.1%
120 1
< 0.1%
161 1
< 0.1%
165 1
< 0.1%
167 1
< 0.1%
171 2
< 0.1%
174 1
< 0.1%
175 1
< 0.1%
182 1
< 0.1%
193 1
< 0.1%
ValueCountFrequency (%)
581 1
< 0.1%
527 1
< 0.1%
524 1
< 0.1%
517 1
< 0.1%
506 1
< 0.1%
504 1
< 0.1%
503 1
< 0.1%
501 1
< 0.1%
500 1
< 0.1%
499 1
< 0.1%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)4.9%
Missing2270
Missing (%)55.9%
Infinite0
Infinite (%)0.0%
Mean39.947486
Minimum1
Maximum614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:03.818623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q127
median40
Q350
95-th percentile68
Maximum614
Range613
Interquartile range (IQR)23

Descriptive statistics

Standard deviation24.382446
Coefficient of variation (CV)0.61036246
Kurtosis279.77493
Mean39.947486
Median Absolute Deviation (MAD)10
Skewness12.542033
Sum71506
Variance594.50367
MonotonicityNot monotonic
2024-02-17T21:37:03.945320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 169
 
4.2%
30 151
 
3.7%
40 120
 
3.0%
25 75
 
1.8%
35 73
 
1.8%
60 69
 
1.7%
55 58
 
1.4%
20 55
 
1.4%
45 53
 
1.3%
32 44
 
1.1%
Other values (77) 923
22.7%
(Missing) 2270
55.9%
ValueCountFrequency (%)
1 5
 
0.1%
2 1
 
< 0.1%
3 6
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 4
 
0.1%
8 6
 
0.1%
9 4
 
0.1%
10 22
0.5%
ValueCountFrequency (%)
614 1
 
< 0.1%
553 1
 
< 0.1%
97 1
 
< 0.1%
90 1
 
< 0.1%
85 4
0.1%
84 2
< 0.1%
82 1
 
< 0.1%
81 2
< 0.1%
80 3
0.1%
78 1
 
< 0.1%

Chad1000x (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)95.9%
Missing4011
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean3856.1224
Minimum2744
Maximum5520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:04.065678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2744
5-th percentile2906.6
Q13380
median3671
Q34307
95-th percentile5147.4
Maximum5520
Range2776
Interquartile range (IQR)927

Descriptive statistics

Standard deviation693.9612
Coefficient of variation (CV)0.17996348
Kurtosis-0.059205134
Mean3856.1224
Median Absolute Deviation (MAD)371
Skewness0.68903041
Sum188950
Variance481582.15
MonotonicityNot monotonic
2024-02-17T21:37:04.185023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4020 2
 
< 0.1%
3300 2
 
< 0.1%
4505 1
 
< 0.1%
3742 1
 
< 0.1%
2744 1
 
< 0.1%
4273 1
 
< 0.1%
3890 1
 
< 0.1%
3634 1
 
< 0.1%
3918 1
 
< 0.1%
2901 1
 
< 0.1%
Other values (37) 37
 
0.9%
(Missing) 4011
98.8%
ValueCountFrequency (%)
2744 1
< 0.1%
2820 1
< 0.1%
2901 1
< 0.1%
2915 1
< 0.1%
2922 1
< 0.1%
3118 1
< 0.1%
3120 1
< 0.1%
3295 1
< 0.1%
3300 2
< 0.1%
3305 1
< 0.1%
ValueCountFrequency (%)
5520 1
< 0.1%
5492 1
< 0.1%
5169 1
< 0.1%
5115 1
< 0.1%
5040 1
< 0.1%
4899 1
< 0.1%
4598 1
< 0.1%
4531 1
< 0.1%
4515 1
< 0.1%
4505 1
< 0.1%

L1 Benchmark (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing4046
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean276.57143
Minimum179
Maximum384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:04.285104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum179
5-th percentile179.65
Q1222
median285.5
Q3318.75
95-th percentile383.35
Maximum384
Range205
Interquartile range (IQR)96.75

Descriptive statistics

Standard deviation68.16125
Coefficient of variation (CV)0.2464508
Kurtosis-1.0164472
Mean276.57143
Median Absolute Deviation (MAD)47.5
Skewness0.08786377
Sum3872
Variance4645.956
MonotonicityNot monotonic
2024-02-17T21:37:04.373370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
206 1
 
< 0.1%
216 1
 
< 0.1%
179 1
 
< 0.1%
240 1
 
< 0.1%
300 1
 
< 0.1%
180 1
 
< 0.1%
243 1
 
< 0.1%
315 1
 
< 0.1%
292 1
 
< 0.1%
320 1
 
< 0.1%
Other values (4) 4
 
0.1%
(Missing) 4046
99.7%
ValueCountFrequency (%)
179 1
< 0.1%
180 1
< 0.1%
206 1
< 0.1%
216 1
< 0.1%
240 1
< 0.1%
243 1
< 0.1%
279 1
< 0.1%
292 1
< 0.1%
300 1
< 0.1%
315 1
< 0.1%
ValueCountFrequency (%)
384 1
< 0.1%
383 1
< 0.1%
335 1
< 0.1%
320 1
< 0.1%
315 1
< 0.1%
300 1
< 0.1%
292 1
< 0.1%
279 1
< 0.1%
243 1
< 0.1%
240 1
< 0.1%

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct422
Distinct (%)78.6%
Missing3523
Missing (%)86.8%
Infinite0
Infinite (%)0.0%
Mean1455.4134
Minimum603
Maximum3504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:04.509157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum603
5-th percentile986.4
Q11196
median1410
Q31650
95-th percentile2091.2
Maximum3504
Range2901
Interquartile range (IQR)454

Descriptive statistics

Standard deviation353.90719
Coefficient of variation (CV)0.24316609
Kurtosis3.5536783
Mean1455.4134
Median Absolute Deviation (MAD)217
Skewness1.2001216
Sum781557
Variance125250.3
MonotonicityNot monotonic
2024-02-17T21:37:04.633854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 7
 
0.2%
1800 4
 
0.1%
1152 4
 
0.1%
1608 4
 
0.1%
1382 3
 
0.1%
1167 3
 
0.1%
1490 3
 
0.1%
1158 3
 
0.1%
1522 3
 
0.1%
1791 3
 
0.1%
Other values (412) 500
 
12.3%
(Missing) 3523
86.8%
ValueCountFrequency (%)
603 1
< 0.1%
610 1
< 0.1%
810 1
< 0.1%
813 1
< 0.1%
818 1
< 0.1%
888 1
< 0.1%
891 1
< 0.1%
896 1
< 0.1%
900 1
< 0.1%
907 1
< 0.1%
ValueCountFrequency (%)
3504 1
< 0.1%
3360 1
< 0.1%
2822 1
< 0.1%
2520 1
< 0.1%
2519 1
< 0.1%
2488 1
< 0.1%
2460 1
< 0.1%
2406 1
< 0.1%
2385 1
< 0.1%
2352 1
< 0.1%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct370
Distinct (%)15.0%
Missing1594
Missing (%)39.3%
Infinite0
Infinite (%)0.0%
Mean223.44363
Minimum97
Maximum1342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:04.758603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile130
Q1158
median195
Q3262.75
95-th percentile407
Maximum1342
Range1245
Interquartile range (IQR)104.75

Descriptive statistics

Standard deviation98.087941
Coefficient of variation (CV)0.43898293
Kurtosis14.975854
Mean223.44363
Median Absolute Deviation (MAD)45
Skewness2.7356587
Sum551012
Variance9621.2441
MonotonicityNot monotonic
2024-02-17T21:37:04.887183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 42
 
1.0%
170 31
 
0.8%
177 31
 
0.8%
144 30
 
0.7%
150 30
 
0.7%
162 30
 
0.7%
165 29
 
0.7%
134 29
 
0.7%
210 26
 
0.6%
180 26
 
0.6%
Other values (360) 2162
53.3%
(Missing) 1594
39.3%
ValueCountFrequency (%)
97 1
 
< 0.1%
109 1
 
< 0.1%
112 1
 
< 0.1%
113 3
0.1%
115 1
 
< 0.1%
117 1
 
< 0.1%
118 4
0.1%
119 5
0.1%
120 6
0.1%
121 4
0.1%
ValueCountFrequency (%)
1342 1
< 0.1%
961 1
< 0.1%
949 1
< 0.1%
932 1
< 0.1%
900 1
< 0.1%
878 1
< 0.1%
874 1
< 0.1%
770 1
< 0.1%
726 1
< 0.1%
723 1
< 0.1%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct296
Distinct (%)15.6%
Missing2163
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean169.54138
Minimum59
Maximum2723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:05.012750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile92
Q1120
median151
Q3190
95-th percentile299.2
Maximum2723
Range2664
Interquartile range (IQR)70

Descriptive statistics

Standard deviation100.83723
Coefficient of variation (CV)0.59476471
Kurtosis238.84933
Mean169.54138
Median Absolute Deviation (MAD)32
Skewness11.21698
Sum321620
Variance10168.147
MonotonicityNot monotonic
2024-02-17T21:37:05.170518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 38
 
0.9%
165 30
 
0.7%
150 28
 
0.7%
178 27
 
0.7%
130 26
 
0.6%
107 21
 
0.5%
152 21
 
0.5%
145 21
 
0.5%
135 21
 
0.5%
119 21
 
0.5%
Other values (286) 1643
40.5%
(Missing) 2163
53.3%
ValueCountFrequency (%)
59 1
 
< 0.1%
60 1
 
< 0.1%
63 1
 
< 0.1%
65 1
 
< 0.1%
67 2
< 0.1%
68 3
0.1%
70 1
 
< 0.1%
72 4
0.1%
73 1
 
< 0.1%
74 2
< 0.1%
ValueCountFrequency (%)
2723 1
< 0.1%
1440 1
< 0.1%
1232 1
< 0.1%
900 1
< 0.1%
806 1
< 0.1%
615 1
< 0.1%
600 2
< 0.1%
590 1
< 0.1%
568 1
< 0.1%
563 1
< 0.1%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct368
Distinct (%)30.4%
Missing2851
Missing (%)70.2%
Infinite0
Infinite (%)0.0%
Mean572.68569
Minimum345
Maximum3252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:05.294885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum345
5-th percentile444
Q1497
median548
Q3618
95-th percentile769
Maximum3252
Range2907
Interquartile range (IQR)121

Descriptive statistics

Standard deviation148.54897
Coefficient of variation (CV)0.25939005
Kurtosis159.26102
Mean572.68569
Median Absolute Deviation (MAD)57
Skewness9.542636
Sum692377
Variance22066.797
MonotonicityNot monotonic
2024-02-17T21:37:05.421916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 12
 
0.3%
480 11
 
0.3%
478 11
 
0.3%
538 10
 
0.2%
540 10
 
0.2%
497 10
 
0.2%
512 10
 
0.2%
525 9
 
0.2%
543 9
 
0.2%
533 9
 
0.2%
Other values (358) 1108
 
27.3%
(Missing) 2851
70.2%
ValueCountFrequency (%)
345 1
< 0.1%
348 1
< 0.1%
359 1
< 0.1%
360 1
< 0.1%
389 1
< 0.1%
394 1
< 0.1%
396 1
< 0.1%
397 1
< 0.1%
403 1
< 0.1%
404 2
< 0.1%
ValueCountFrequency (%)
3252 1
< 0.1%
3124 1
< 0.1%
1333 1
< 0.1%
1200 1
< 0.1%
1170 1
< 0.1%
987 1
< 0.1%
976 1
< 0.1%
944 1
< 0.1%
916 1
< 0.1%
897 1
< 0.1%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct586
Distinct (%)37.3%
Missing2491
Missing (%)61.4%
Infinite0
Infinite (%)0.0%
Mean1358.1969
Minimum828
Maximum3120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:05.549207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum828
5-th percentile1094.4
Q11200
median1320
Q31468
95-th percentile1780.6
Maximum3120
Range2292
Interquartile range (IQR)268

Descriptive statistics

Standard deviation216.87828
Coefficient of variation (CV)0.15968103
Kurtosis5.2016041
Mean1358.1969
Median Absolute Deviation (MAD)125
Skewness1.4767626
Sum2131011
Variance47036.19
MonotonicityNot monotonic
2024-02-17T21:37:05.696112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320 41
 
1.0%
1260 36
 
0.9%
1200 36
 
0.9%
1380 32
 
0.8%
1500 22
 
0.5%
1440 22
 
0.5%
1170 20
 
0.5%
1290 18
 
0.4%
1800 18
 
0.4%
1230 16
 
0.4%
Other values (576) 1308
32.2%
(Missing) 2491
61.4%
ValueCountFrequency (%)
828 1
< 0.1%
866 1
< 0.1%
881 1
< 0.1%
888 1
< 0.1%
900 1
< 0.1%
920 1
< 0.1%
925 1
< 0.1%
949 1
< 0.1%
971 1
< 0.1%
977 1
< 0.1%
ValueCountFrequency (%)
3120 1
< 0.1%
2700 1
< 0.1%
2400 2
< 0.1%
2390 1
< 0.1%
2341 1
< 0.1%
2280 1
< 0.1%
2273 1
< 0.1%
2160 1
< 0.1%
2153 1
< 0.1%
2107 1
< 0.1%

Sprint 400m (s)
Real number (ℝ)

MISSING 

Distinct80
Distinct (%)8.3%
Missing3100
Missing (%)76.4%
Infinite0
Infinite (%)0.0%
Mean70.0125
Minimum45
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2024-02-17T21:37:05.860787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile52
Q159
median65
Q375
95-th percentile100
Maximum300
Range255
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.024798
Coefficient of variation (CV)0.28601747
Kurtosis43.913785
Mean70.0125
Median Absolute Deviation (MAD)7
Skewness4.9382156
Sum67212
Variance400.99254
MonotonicityNot monotonic
2024-02-17T21:37:06.057406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 63
 
1.6%
70 58
 
1.4%
65 54
 
1.3%
58 38
 
0.9%
59 38
 
0.9%
56 34
 
0.8%
61 33
 
0.8%
55 31
 
0.8%
75 30
 
0.7%
67 30
 
0.7%
Other values (70) 551
 
13.6%
(Missing) 3100
76.4%
ValueCountFrequency (%)
45 1
 
< 0.1%
46 1
 
< 0.1%
47 2
 
< 0.1%
48 2
 
< 0.1%
49 6
 
0.1%
50 10
 
0.2%
51 9
 
0.2%
52 25
0.6%
53 13
0.3%
54 21
0.5%
ValueCountFrequency (%)
300 2
< 0.1%
240 1
 
< 0.1%
180 1
 
< 0.1%
179 1
 
< 0.1%
178 1
 
< 0.1%
160 2
< 0.1%
135 1
 
< 0.1%
134 1
 
< 0.1%
130 1
 
< 0.1%
125 3
0.1%

Interactions

2024-02-17T21:36:58.104408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.081301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.484490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.904738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.220506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.566842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.028853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.325626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.647832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.076476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.212825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.524790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.854391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.428832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.814454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.204811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.194065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.589581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.000197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.316222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.661675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.125184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.423615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.721267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.154908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.325077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.623908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.955354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.539403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.907831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.300084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.292491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.684231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.089000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.404969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.756724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.206451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.512281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.796708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.222946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.412404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.710433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.055064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.635620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.988884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.401466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.381567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.768959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.168940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.487217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.846596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.299290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.608493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.885109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.296867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.508796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.794459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.153179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.741267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.082783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.497143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.473707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.855506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.255284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.571995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.937642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.385528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.694251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.964377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.396052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.595680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.884971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.249880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.859908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.164872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.595422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.566046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.945352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.351868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.677687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.021514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.480944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.783589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.047973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.466204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.683122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.995599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.347527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.960444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.248482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.675471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.663637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.030189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.443555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.771149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.115134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.568731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.867579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.131974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.553903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.759101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.089795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.434777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.047060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.329993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.757218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.765222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.119566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.537087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.866812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.211701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.651918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.957449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.224380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.624620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.851015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.180853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.526866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.136699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.424505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.831470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.854329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.198420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.615396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.947655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.291720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.727477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.035738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.309954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.704038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.924368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.255551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.599914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.208738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.496003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.897113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:39.928395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.266080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.682306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.022917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.491728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.814526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.101659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.387138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.769172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.008168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.323074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.669495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.295102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.561589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.978221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.019306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.353052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.774309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.114968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.578586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.892608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.188921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.465192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.845484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.094681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.403911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:54.757200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.379351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.637634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:59.063471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.112841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.440023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.861742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.206636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.671233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:46.987903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.283216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.737953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.917296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.171012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.491926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.051236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.469467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.743019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:59.147619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.204456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.533857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:42.945376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.300747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.761381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.078685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.378680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.815398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:50.994008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.260367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.583901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.135857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.558038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.844882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:59.225724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.292509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.621537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.036885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.390644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.851725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.157839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.465747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.910413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.077517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.339001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.670281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.226824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.642583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:57.932366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:59.316354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:40.382790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:41.809075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:43.125076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:44.471425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:45.934785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:47.241937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:48.564914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:49.994662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:51.146298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:52.438377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:53.769720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:55.336430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:56.733740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:36:58.011658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T21:37:06.167886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Chad1000x (s)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Grace (s)Helen (s)L1 Benchmark (s)Max Pull-upsRankRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.000-0.0060.8370.8000.481-0.287-0.582-0.633-0.333-0.1950.342-0.432-0.0720.777-0.191
Chad1000x (s)-0.0061.000-0.148-0.091-0.3050.5850.3700.0470.2171.000-0.2740.3070.349-0.1530.321
Clean and Jerk (lbs)0.837-0.1481.0000.7480.553-0.358-0.641-0.699-0.394-0.3620.449-0.540-0.1410.904-0.211
Deadlift (lbs)0.800-0.0910.7481.0000.464-0.197-0.498-0.608-0.2980.0520.299-0.392-0.1180.677-0.157
Fight Gone Bad0.481-0.3050.5530.4641.000-0.701-0.666-0.627-0.658-0.2000.498-0.584-0.3960.544-0.410
Filthy 50 (s)-0.2870.585-0.358-0.197-0.7011.0000.6060.4220.666-0.500-0.5360.5460.492-0.3560.459
Fran (s)-0.5820.370-0.641-0.498-0.6660.6061.0000.6550.6470.382-0.6580.6360.337-0.6160.342
Grace (s)-0.6330.047-0.699-0.608-0.6270.4220.6551.0000.4950.667-0.4440.4530.224-0.6610.274
Helen (s)-0.3330.217-0.394-0.298-0.6580.6660.6470.4951.0000.643-0.5900.5520.511-0.3890.473
L1 Benchmark (s)-0.1951.000-0.3620.052-0.200-0.5000.3820.6670.6431.000-0.4940.8200.164-0.2400.378
Max Pull-ups0.342-0.2740.4490.2990.498-0.536-0.658-0.444-0.590-0.4941.000-0.528-0.3480.470-0.383
Rank-0.4320.307-0.540-0.392-0.5840.5460.6360.4530.5520.820-0.5281.0000.347-0.5530.308
Run 5k (s)-0.0720.349-0.141-0.118-0.3960.4920.3370.2240.5110.164-0.3480.3471.000-0.1480.488
Snatch (lbs)0.777-0.1530.9040.6770.544-0.356-0.616-0.661-0.389-0.2400.470-0.553-0.1481.000-0.217
Sprint 400m (s)-0.1910.321-0.211-0.157-0.4100.4590.3420.2740.4730.378-0.3830.3080.488-0.2171.000

Missing values

2024-02-17T21:36:59.469664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T21:36:59.752696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
57071Tola MorakinyoCrossFit East NashvilleNaNworldwideMen2.0worldwideopen500.0000390.00000615.0000340.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57173Andrea BarbottiCrossFit SenigalliaNaNworldwideMen56.0worldwideopen407.8547313.05604462.9702249.12206422.040.0NaNNaNNaN140.0145.0NaNNaNNaN
57178Niklas HechtCrossFit OBANaNworldwideMen66.0worldwideopen475.0000355.00000525.0000285.00000NaN68.0NaNNaNNaN121.068.0NaNNaNNaN
57179Sam StewartCrossFit WalleyeNaNworldwideMen68.0worldwideopen451.9471352.73920551.1550286.60060NaN50.0NaNNaNNaN130.086.0486.0NaNNaN
57180Nick TunziFort to Fort CrossFitNaNworldwideMen88.0worldwideopen520.0000350.00000540.0000280.00000NaNNaNNaNNaNNaNNaN80.0NaNNaN65.0
57181Santiago CombaSense Fitness CrossFitNaNworldwideMen116.0worldwideopen462.9702337.30686529.1088264.55440NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57182Logan CollinsCrossFit Franco'sNaNworldwideMen130.0worldwideopen455.0000340.00000535.0000275.00000NaN90.0NaNNaNNaN122.065.0NaN1080.0NaN
57183Brandon LuckettCrossFit Franco'sNaNworldwideMen138.0worldwideopen415.0000335.00000485.0000275.00000NaNNaNNaNNaNNaN156.0NaN448.0NaNNaN
57184Brandon SwanCrossFit TorianNaNworldwideMen139.0worldwideopen440.9240354.94382551.1550293.21446410.070.0NaNNaN1075.0123.080.0468.0NaN58.0
57185Raphael DurandRhino CrossFitNaNworldwideMen151.0worldwideopen455.0000325.00000550.0000265.00000NaNNaNNaNNaNNaN136.094.0482.01231.0NaN
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
61226Lucas PelsterCrossFit MillardNaNworldwideMen149414.0worldwideopen255.0000165.00000300.0000115.00000264.0NaNNaNNaNNaNNaN300.0NaNNaNNaN
61227Evan GreenLions Bay CrossFitNaNworldwideMen149448.0worldwideopen330.6930198.41580396.8316187.39270NaNNaNNaNNaNNaNNaN326.0NaN1843.0NaN
61228Braden RayburnCrossFit PerseveranceNaNworldwideMen149633.0worldwideopen465.0000315.00000635.0000225.00000NaNNaNNaNNaNNaN229.0203.0585.0NaN68.0
61229Christopher BurkettTowpath CrossFitNaNworldwideMen149948.0worldwideopen175.0000135.00000205.0000105.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61230Marcus PallvidCrossFit TrollhättanNaNworldwideMen150939.0worldwideopenNaN165.34650NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61231Mehdi ZourmandCrossFit LeylandNaNworldwideMen152619.0worldwideopen198.4158165.34650352.7392121.25410NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61232Joel FlugerCrossFit CortlandNaNworldwideMen153121.0worldwideopenNaNNaN355.0000NaNNaNNaNNaNNaNNaNNaNNaNNaN2153.0NaN
61233Vincenzo MartiranoCrossFit TreeHouseNaNworldwideMen153679.0worldwideopen385.0000275.00000410.0000190.00000NaN35.0NaNNaNNaN245.0148.0NaNNaNNaN
61234Tim BielikCrossFit PolarisNaNworldwideMen155374.0worldwideopen200.0000150.00000295.0000105.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61235Jorge Hierro RamosDuque de Alba CrossFitNaNworldwideMen157102.0worldwideopen440.9240326.28376507.0626235.89434NaN50.0NaNNaNNaN170.0393.0NaN1200.056.0

Duplicate rows

Most frequently occurring

AthleteAffiliateRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)# duplicates
0Alejandro RiosCrossFit ElectricaworldwideMen16654.0worldwideopen415.00000300.00000485.00000225.00000366.030.0NaNNaNNaN194.0176.0553.01468.062.02
1Andrew ShengFoundation CrossFitworldwideMen91142.0worldwideopen304.23756227.07586403.44546160.93726207.08.0NaNNaNNaN414.0351.0866.01464.0NaN2
2Arian RoehrleCrossFit Schwäbisch GmündworldwideMen45238.0worldwideopen264.55440194.00656341.71610165.34650303.027.0NaNNaNNaN417.0NaN645.01573.0NaN2
3Carlos Semmer CostaInsist CrossFitworldwideMen74148.0worldwideopen370.37616264.55440529.10880154.32340NaN20.0NaNNaN1774.0NaN159.0NaN1841.0NaN2
4Cleber MagnagoReino CrossFitworldwideMen60156.0worldwideopen255.00000175.00000315.00000125.00000NaN22.0NaNNaNNaNNaNNaNNaN1527.0125.02
5Drew JohnstonCrossFit 8035 WestworldwideMen12952.0worldwideopen435.00000275.00000460.00000215.00000405.0NaNNaNNaN1464.0156.0109.0505.01408.0NaN2
6Evaldo SilvaNaNworldwideMen2490.0worldwideopen451.94710352.73920529.10880275.57750NaN40.0NaNNaNNaNNaN123.0NaN1488.0NaN2
7Ghewy DamienBlue Lion CrossFitworldwideMen85279.0worldwideopen264.55440202.82504374.78540158.73264NaN30.0NaNNaNNaNNaNNaNNaN1620.0NaN2
8Jerry KellyNaNworldwideMen90273.0worldwideopen385.00000220.00000460.00000185.00000222.022.0NaNNaN2159.0321.0211.0623.01337.0102.02
9Joseph GazzardCrossFit MerciaworldwideMen49413.0worldwideopen396.83160297.62370462.97020187.39270NaNNaNNaNNaNNaN463.0210.0NaN1560.0NaN2